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At some point, many scientists working in geographic fields may have asked themselves if there is any “use” in the work we do. The main aim of a study should not be to get it published in a high impact journal but to share research results with as many people as possible. However, here comes the dilemma, high impact journals are recognized by a much wider audience, so in the end it does matter where the study is published. Our paper just published in Nature Sustainability made it into the News and Views section of Nature with the nice title “Satellite images show China going green“. It is a great summary of our work and helps to reach an even larger audience.

So what makes this work so attractive? The human footprint in satellite images is traditionally linked with degradation, conversion of forest areas into farmlands or urban areas and air pollution. Especially large population countries with a boosting economy, like China, are usually linked with the destruction of the environment. However, here we show that management and conservation activities in China can lead to a large increase in vegetation cover and carbon stocks, in spite of drought conditions. The observed increase in vegetation growth does not only improve the ecological environment by alleviating degradation, but also the magnitude of increase is found to be large enough to contribute to a greening Earth and store large amounts of carbon.

We are happy that this message made it into the Nature News section, which is certainly among the top places where scientific results can end up. It is a nice confirmation that what we do does matter and is recognized at highest levels.

Now this is something really cool:
-We have a paper in the first ever issue of the new journal Nature Sustainability
-Both the cover of the first issue and the website banner are my photos I shot last summer in Southern China
-The paper is the thesis of my girlfriend and it is the cover story!

In November 2015, me and a colleague (Michael Degerald, visit his blog here) asked the question: how is agriculture affected in the areas seized by the Islamic State (aka ISIS, ISIL, Da’esh)? We couldn’t find much information to answer our question, so we decided to investigate it ourselves.

At first we wanted to look at changes in productivity indicated by satellite measured greenness, but later we decided to go a step deeper and look at land use activity as an indicator of land abandonment (as I had done in a previous publication). As the project moved on, more people became interested, and eventually three more co-authors were added: Petter Pilesjö (Lund University), Martin Brandt and Alexander Prishcepov (both from Copenhagen University).

Together, we conducted a land use classification based on NDVI data from MODIS based on the seasonality of the land surface. We distinguished between single cropped cropland…

People who know me and my research know that I am in love with the Sahel and its people, and this will never change. However, I recently had the chance to expand my research area from the semi-arid Sahel to humid China, more specifically the South China Karst. I spent around 4 months in China in 2017, and there are some major publications on the way. This area is particularly interesting, because millions of trees have been planted, and while we have a hard time to find a human footprint in satellite data over Sahel, it is more than obvious in China. It is also a very beautiful area:

In total, 24 articles were submitted, 13 of them were published, 6 rejected without going to review, and 5 were rejected after review.

So we have an acceptance rate of 54%. Interestingly, this is very close to the 2013 statistics for all submissions, and it also reflects the overall quality of the submissions, which is average. The quality of the articles that were published in the end is ok, some are good, but rather not exceptional.

The interaction between us and the MDPI editorial staff was professional, smooth and efficient. Everything was prepared nicely for us and we could concentrate on the scientific part without any managing aspects.

Having now 7 articles published in this journal (2 as first author) within the past 3 years, I can fully recommend publishing in Remote Sensing. Yes, the quality of the articles can not be compared with the leading journal “Remote Sensing of Environment” (here we have 4 articles now published within the past 2 years), and it is for sure easier to publish in Remote Sensing (RS) with less critical editors and reviewers. However, if you have an overall good quality article (not exceptional), there are several reasons for going for RS instead for the armada of Elsevier and Springer journals:

Open access: research should be available to everyone and not limited to rich countries and rich universities. Remote Sensing of Environment for example is not available at my former university, and in many German universities Elsevier journals are generally unavailable. Buying open access in these journals is possible but too expensive. Thousands of academics boycott Elsevier.

The authors of the article keep the rights on their research and are able to distribute their work freely.

Rapid processing, most of our 7 articles were published after around 2 months. The main reason is the professional editorial staff who do this work as full time job.

The articles are downloaded thousands of times and reach a wide audience.

One may argue the large number of average quality articles being published (being an online only journal, there are no issues and thus no article limit) swamps the scientific market and reduces importance of individual scientific work, but this is a general problem of science these days. One may also argue that the publisher MDPI is a company making money with each article they publish (and there is no limitation), so their aim is probably to publish as many articles as possible, and this is not beneficial for being critical. This might be true, however, in the end it’s up to the academic editors and the reviewers to decide if an article is published, not the company, and even the Nature and Science groups have their own mass publishing journals (Scientific Reports, Science Advances) now. Scientific publishing is about making profit.

Many people think that open access journals like RS are commercial companies making money (“you pay to get your paper published”), whereas articles published in Elsevier & co are non-commercial and real science. Here one should not forget that companies like Elsevier make billions of $$ profit each year, of which the reviewers see nothing and the editors do it as free time job being poorly paid. The universities pay absurd sums to make the articles available for their students, but many universities can not, and do not want to support this any more, but rather support the open access publishing by paying the publishing waves. In the end, this is much cheaper for the university and the article is freely available for everyone.

My personal recommendation: If you think you have an exceptional article dealing with remote sensing, there is no way around Remote Sensing of Environment, the reputation of this journal is untouchable. However, not every study we do has outstanding results, so if you do not want to wait more than a half year for a likely rejection, I personally can fully recommend Remote Sensing, the processing is rapid but still professional.

I’m not a fan of Google, but this is something people interested in satellite images should not miss: Google Earth has its 10th anniversary and provides 1500 stunning satellite images for free: https://earthview.withgoogle.com/

Correlations are a very famous and popular way to express relationships (and their strength) between two variables. Applications in environmental sciences span from relations of satellite based parameters with ground observations, to relationships between parameters like vegetation and precipitation. Furthermore, scientists use correlations to find linkages between totally different datasets of different scientific disciplines and spatial scales, e.g. migration and environment. However, many scientists blindly trust these statistical analyses and even low correlations are often interpreted in an awkward and very speculative way without questioning the results.

Too much reliance on statistical parameters can be dangerous, as you can have a strong correlation between two variables that are not related. This is shown in this website where, for example, the Per capita consumption of margarine (US) is correlated with Divorce rate in Maine at a correlation coefficient of 0.992558. How would you interpret such a relationship? Does this prove that married people shouldn’t eat margarine? It’s a nonsense correlation, these two variables simply happened to occur during the same years (the correlation was based on time, not space). In this relationship, the scale problems are quite obvious. First of all, the variables do not have the same spatial extent, even though they overlap in Maine. Also the temporal detail can be questioned. Many things happen during a year, so how would this correlation look at a finer temporal detail, for example monthly? We’re sure it would not be as strong.

Strong correlations can often be found between variables that are not directly linked, especially when the spatial and temporal details are coarse (e.g. nationwide, yearly).

The interpretation of statistical analysis outputs can be a challenge and therefore it is important to make sure that you know what you’re doing. Furthermore, the output values should be interpreted usingcommon senseand an awareness of how scale issues might affect the results.

RGB compositions are very important in remote sensing studies, however, there doesn’t seem to be a function in QGIS. But there is an easy trick by creating a simple text file which stores the paths to images.

So go to “Raster – Miscellaneous – Build Virtual Raster (Catalog)”

pick the 3 raster layers, tick “Separate” and define an output file. That’s it. Now the text file can be loaded and the RGB displayed, like this RapidEye image with NDVI as red and bands 3 and 2 as green and blue.